NBA Rankings Midseason blowout

Always read something that will make you look good if you die in the middle of it.
-P. J. O’Rourke

Did you miss me? I missed you too. We’ve been busy in the lab for the past few weeks. Some of what we came up with you’ve seen (The MVP Equation), some of it you should have seen (team charts via the NBA Geek) and some of it you won’t be seeing quite yet (Hint: March is around the corner).

The important bit is that we’ve hit the halfway point of the season. In view of that, I’m blowing it out and giving you all the numbers and tables you can handle.

Let’s start with everyone’s point margin adjusted for homecourt advantage and opponent.

You’ll note that the table has a running total by time period (season, last 30, last 25, last 20, last 15, last 10, last 5, and last 2). This is meant to showcase the ebbs and flows of each team as the season goes along. It does however get more informative if I:

Point Margin per Game: (Pts scored by team) – (Pts scored by opponent) / games played

Home court Point Margin per Game: Point Margin per game due to the schedule and homecourt advantage.

Adjusted Point Margin per Game: (Point Margin per Game) – (Home court Point Margin per Game). Schedule independent point margin (neutral site at sea level)

Adjusted Opponent Point Margin: The average Point Margin per Game of a teams opponents.

Real Point Margin (RPM): (Point Margin per Game) – (Home court Point Margin per Game) + (Adjusted Opponent Point Margin). Expected Point Margin at a neutral site against perfectly average opposition. This is the Number I use to rank. Please note that I added the RPM for the last ten games to give me a more real time weighed estimate.

Neutral Site Win % : A win projection using the real point margin and the relationship between point margin and win% (RPM/31 + .500 is a quick shorthand but not quite right, we gotta have some secrets)

As always, keep in mind that this is a guess (buyer beware) at the relative strengths of teams based on the data of the season to date with some weighing put in for more recent games. A more accurate projection would account for injuries and incorporate what we know of player historical performance. We will address this in a, say it with me, future post before the playoffs.

And for the League:

Ladies and gentlemen, we have movement at the top! Let’s do some notes:

While the West is still very, very strong with 3 of the 4 top teams in the league in a dead heat, Miami has come back to form and moved back into that top contender tier. If I had to guess, San Antonio gets the top seed out West but it’s all coming down to health and luck in the end.

Chicago is lurking (two words : Jimmy Butler) and so is Derrick Rose. Let’s keep an eye out shall we? The possible Stat Revival in New York also bears watching. Bulls/Knicks in Round 2? Seems inevitable.

The rest of the East so far is horrid. The records are more than a bit inflated. What is intriguing is the possible turnaround of a few teams via trade (Pistons) or return from injury (Sixers, Wizards) while others collapse to the lottery (Raptors and, it kills me to type this, Celtics). I actually think the Pistons are the best bet to improve dramatically in the second half, particularly if Drummond can overcome his tragic case of drafted-too-low-itis and stay on the court.

The Lakers are sitting at 9th overall and seventh in the West. They needed Minnesota, Utah and Portland to falter. Minnesota is most likely the victim of Eduardo the injury fairy taking a transfer from Portland. Portland is about to be victimized by the league office. Utah just got clowned by the Rockets. Throw in some significant economic incentives and I feel the Lakers are almost a dead lock to get the eight seed.

Let’s show you the full season simulation now. You’ll note that I did the sim based on the full season, the last ten games and the average of the two.

And as always that brings us to current projections sorted as minimum, average and maximum projected wins. The table also shows the numbers from the preseason projection and whether that number was low, high or in range based on the current status of the league.

18 of the 30 projections are in line with the preseason numbers. For the lows, we have Chicago (who seem to have found a real gem in Butler), Indiana (Stephenson is a surprise), Brooklyn (Brook Lopez learning how to rebound), Detroit (and Drummond overcoming CWPMS — Coach Won’t Play Me Syndrome), the West top three (and the continued underestimated level of suck in the East), and Golden State (David Lee with the two year recovery from zombification). As for the highs, we have Philly (Bynum and the Bowling hijinks), Minnesota and New Orleans featured in injurypalooza 2012-13 as for Toronto? Well, get to know Bryan Colangelo.

As noted earlier, the Lakers are lurking around that last spot in the West and need some real luck to make it in. If it were any other team, I wouldn’t like their odds. I just don’t think the Angel of Stern is going to Kobe take an early vacation at this point.

Playoff odds anyone?

Before the season, I picked San Antonio over Miami in the finals. I see no reason to change my mind.

-Arturo

P.S. Again as a bonus, Here’s a table presented without explanation or context:

19 Responses to "NBA Rankings Midseason blowout"

I still think Kevin Durant and Tim Duncan will regress in the 2nd half of the season, and Lamar Odom will continue to get better. Hopefully, he’ll stop missing shots. I think the clippers give good enough reason to have them in the finals.

Nice piece. Chi is most surprising to me, especially because Butler hasn’t been playing that many minutes until recently.
I was wondering where I could find the player graph showing POP (and maybe WP48) over time (like the one with Stat above). I can’t seem to find it on NBA Geek.
Thanks

Here’s what I find fascinating – in almost every case, the actual winning percentage for good teams (>.500) far exceeds their theoretical (“played like”) winning percentage, while the actual winning percentage for bad teams (<.500) is far below their theoretical ("played like") winning percentage. While there are a few exceptions (HOU is over .500 but playing worse than expected, BOS, PHL, and SAC are playing about as expected), it seems that the actual winning percentages are exaggerated as compared to the expected winning percentages. Any idea why? Is there a problem with the expected winning percentges, or is this just a freaky year?

The other three teams I see, but why on earth would Denver make that trade? I’d be happy to give up Chandler, but why do they want to give up two reasonably efficient players in Miller and Gallinari for two expensive 35 year old players who are both putting up their worst numbers in a decade? Sorry, but Masai Ujiri is way too smart to end up on the short end of a 4-team deal.

Just one question. Based upon your model how can you forecast the Lakers to make the playoffs? One table has them at 27% and the midpoints point of your expected wins table has them three games behind Utah for 8th place. I’m not saying that they can’t make the playoffs but it’s hard to make a case for them making the playoffs based upon your data.

Good stuff as always, just wondering what your basis is for giving the full season and last 10 games equal weight when it comes to point margin. I guess it helps adjust for injuries and trades, but I think there is also a lot of small sample size randomness you are letting in, and the problem would be better solved by either giving the full season more weight or only emphasizing last 10 on a case by case basis where there have been changes that will continue long term as the season moves forward.

Looks like my comment got eaten, Don’t see the point of averaging L10 and season, especially with equal weight, unless you are contending that a team’s last 10 games are a big enough sample to be equally predictive of future point margin as a half season of data. In effect you are saying at 40 games in, each of the last 10 games should be weighted as 5 times more important than the previous games, and as games played increases the last 10 only get more and more exaggerated. [(Sum 30 previous games PM/40) + (Sum L10 games PM/40) + (Sum L10 games PM/10)] = [Sum previous games PM + 5(Sum L10 games PM)]/40 total games. I just don’t buy this, as I said in the disappeared comment, it might “keep think interesting” by injecting movement in the rankings, but its just artificial “enhancement”.

You’re right but they had a very weak schedule. In those 10 games they played Charlotte, Washington, Phoenix, Minnesota, New Orleans, Philadelphia. Plus struggling or kinda-struggling teams like Dallas and Atlanta. We all know Popovich is a black wizard and basketball genius, Tony Parker is possible the most underrated player in the NBA since many years, etc., but to win against OKC and Miami they need a 2000 Duncan, not a 2012 Duncan. We all saw what has happened in the last years as Duncan can’t play 40 minutes per game in the playoffs anymore and isn’t the elite rim protector/rebounder/scorer he once was.
When they were winning championships he averaged something like 25/15 with 3 blocks.. I like Thiago Splitter since he was playing with Tau but you won’t stop Durant/Westbrook/LeBron with him and Old Duncan.
Too often we forget that the regular season numbers don’t exactly translate into the playoffs, there are a few, but key, variables not taken into account.

[…] Last time around, commenter T recommended that I use a Kalman Filter rather than a simple average of the season and the last ten games. ( Editors Note- A much more in-depth look at a similar method can be seen here care of Jirka Poropudas) This sounded simpler than it was but I did it anyway because it was a really good idea. The reason for the complexity came from the fact that this technique is sensitive to the ordering of the inputs and isn’t as sensitive as i’d like to variations. I fixed that by setting some controls to reset the gain on the input for large variations to the team value. I also built in a filter to protect against outliers ( Houston’s 45 point win in Utah on 1/28/2013 comes to mind) warping the results unnecessarily. These games will still be considered but a cap was placed against the actual maximum impact of a single game. What the result of all this work? […]

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